

'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
A_							    =				logging.get_logger(__name__)





def  A_	(			snake_case   ,							snake_case=False			):
  SCREAMING_SNAKE_CASE:Optional[Any]       =       []
  for i in range(config.num_hidden_layers			):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')			)
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')			)
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')			)
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')			)
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')			)
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')			)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')			)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')			)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')			)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')			)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("cls_token", "vit.embeddings.cls_token"),
          ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
          ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
          ("pos_embed", "vit.embeddings.position_embeddings"),
      ]			)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
            ("pre_logits.fc.weight", "pooler.dense.weight"),
            ("pre_logits.fc.bias", "pooler.dense.bias"),
        ]			)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    SCREAMING_SNAKE_CASE:Optional[Any]       =       [(pair[0], pair[1][4:]) if pair[1].startswith("vit"			) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("norm.weight", "vit.layernorm.weight"),
            ("norm.bias", "vit.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]			)

  return rename_keys





def  A_	(			snake_case   ,							snake_case   ,							snake_case=False			):
  for i in range(config.num_hidden_layers			):
    if base_model:
      SCREAMING_SNAKE_CASE:Any       =       """"""
    else:
      SCREAMING_SNAKE_CASE:List[Any]       =       """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    SCREAMING_SNAKE_CASE:Any       =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
    SCREAMING_SNAKE_CASE:Optional[Any]       =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
    # next, add query, keys and values (in that order) to the state dict
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       in_proj_weight[
        : config.hidden_size, :
    ]
    SCREAMING_SNAKE_CASE:Optional[Any]       =       in_proj_bias[: config.hidden_size]
    SCREAMING_SNAKE_CASE:Any       =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    SCREAMING_SNAKE_CASE:Dict       =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    SCREAMING_SNAKE_CASE:Optional[Any]       =       in_proj_weight[
        -config.hidden_size :, :
    ]
    SCREAMING_SNAKE_CASE:str       =       in_proj_bias[-config.hidden_size :]





def  A_	(			snake_case			):
  SCREAMING_SNAKE_CASE:Dict       =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(snake_case   ,							snake_case			)





def  A_	(			snake_case   ,							snake_case   ,							snake_case			):
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       dct.pop(snake_case			)
  SCREAMING_SNAKE_CASE:List[Any]       =       val





def  A_	(			):
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE:int       =       Image.open(requests.get(snake_case   ,							stream=snake_case			).raw			)
  return im





@torch.no_grad()
def  A_	(			snake_case   ,							snake_case			):
  SCREAMING_SNAKE_CASE:str       =       ViTConfig()
  SCREAMING_SNAKE_CASE:Optional[Any]       =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    SCREAMING_SNAKE_CASE:List[str]       =       True
    SCREAMING_SNAKE_CASE:Optional[Any]       =       int(vit_name[-12:-10]			)
    SCREAMING_SNAKE_CASE:int       =       int(vit_name[-9:-6]			)
  else:
    SCREAMING_SNAKE_CASE:int       =       1000
    SCREAMING_SNAKE_CASE:Any       =       """huggingface/label-files"""
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       """imagenet-1k-id2label.json"""
    SCREAMING_SNAKE_CASE:Dict       =       json.load(open(hf_hub_download(snake_case   ,							snake_case   ,							repo_type="dataset"			)   ,							"r"			)			)
    SCREAMING_SNAKE_CASE:Tuple       =       {int(snake_case			): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Optional[int]       =       idalabel
    SCREAMING_SNAKE_CASE:int       =       {v: k for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       int(vit_name[-6:-4]			)
    SCREAMING_SNAKE_CASE:Optional[Any]       =       int(vit_name[-3:]			)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("tiny"			):
      SCREAMING_SNAKE_CASE:List[str]       =       192
      SCREAMING_SNAKE_CASE:int       =       768
      SCREAMING_SNAKE_CASE:Optional[int]       =       12
      SCREAMING_SNAKE_CASE:str       =       3
    elif vit_name[9:].startswith("small"			):
      SCREAMING_SNAKE_CASE:List[Any]       =       384
      SCREAMING_SNAKE_CASE:Optional[Any]       =       1536
      SCREAMING_SNAKE_CASE:List[Any]       =       12
      SCREAMING_SNAKE_CASE:Optional[Any]       =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("small"			):
      SCREAMING_SNAKE_CASE:int       =       768
      SCREAMING_SNAKE_CASE:Optional[Any]       =       2304
      SCREAMING_SNAKE_CASE:str       =       8
      SCREAMING_SNAKE_CASE:Optional[Any]       =       8
    elif vit_name[4:].startswith("base"			):
      pass
    elif vit_name[4:].startswith("large"			):
      SCREAMING_SNAKE_CASE:List[Any]       =       1024
      SCREAMING_SNAKE_CASE:List[Any]       =       4096
      SCREAMING_SNAKE_CASE:Optional[int]       =       24
      SCREAMING_SNAKE_CASE:Tuple       =       16
    elif vit_name[4:].startswith("huge"			):
      SCREAMING_SNAKE_CASE:Dict       =       1280
      SCREAMING_SNAKE_CASE:Optional[Any]       =       5120
      SCREAMING_SNAKE_CASE:List[Any]       =       32
      SCREAMING_SNAKE_CASE:str       =       16

    # load original model from timm
  SCREAMING_SNAKE_CASE:int       =       timm.create_model(snake_case   ,							pretrained=snake_case			)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       timm_model.state_dict()
  if base_model:
    remove_classification_head_(snake_case			)
  SCREAMING_SNAKE_CASE:Dict       =       create_rename_keys(snake_case   ,							snake_case			)
  for src, dest in rename_keys:
    rename_key(snake_case   ,							snake_case   ,							snake_case			)
  read_in_q_k_v(snake_case   ,							snake_case   ,							snake_case			)

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    SCREAMING_SNAKE_CASE:Tuple       =       ViTModel(snake_case			).eval()
  else:
    SCREAMING_SNAKE_CASE:str       =       ViTForImageClassification(snake_case			).eval()
  model.load_state_dict(snake_case			)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    SCREAMING_SNAKE_CASE:List[str]       =       DeiTImageProcessor(size=config.image_size			)
  else:
    SCREAMING_SNAKE_CASE:Any       =       ViTImageProcessor(size=config.image_size			)
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       image_processor(images=prepare_img()   ,							return_tensors="pt"			)
  SCREAMING_SNAKE_CASE:str       =       encoding["""pixel_values"""]
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       model(snake_case			)

  if base_model:
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       timm_model.forward_features(snake_case			)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(snake_case   ,							outputs.pooler_output   ,							atol=1e-3			)
  else:
    SCREAMING_SNAKE_CASE:List[str]       =       timm_model(snake_case			)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(snake_case   ,							outputs.logits   ,							atol=1e-3			)

  Path(snake_case			).mkdir(exist_ok=snake_case			)
  print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''			)
  model.save_pretrained(snake_case			)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
  image_processor.save_pretrained(snake_case			)


if __name__ == "__main__":
    A_							    =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--vit_name",
        default="vit_base_patch16_224",
        type=str,
        help="Name of the ViT timm model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    A_							    =				parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






